Module panama.ml.tunable.base

Classes

class BaseTunableModel

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class BaseTunableModel(ABC):
    """A base class for tunable machine learning models.

    This class defines a set of abstract methods that must be implemented by
    any concrete subclass.

    Attributes:
        None.
    """

    @abstractmethod
    def fit(self, X: DataFrame, y: Union[DataFrame, Series]):
        """Fit the model to the training data.

        Args:
            X: The training data.
            y: The target values.

        Returns:
            None.
        """
        raise NotImplementedError("Subclass must implement fit method.")

    @abstractmethod
    def predict(self, X: DataFrame):
        """Predict target values for the given data.

        Args:
            X: The input data.

        Returns:
            y_pred: The predicted target values.
        """
        raise NotImplementedError("Subclass must implement predict method.")

    @abstractmethod
    def set_params(self, params: Dict):
        """Set the hyperparameters of the model.

        Args:
            params: hyperparameters and their values.

        Returns:
            None.
        """
        raise NotImplementedError("Subclass must implement set_params method.")

    @abstractmethod
    def get_params(self, deep: bool):
        """Set the hyperparameters of the model.

        Args:
            params: hyperparameters and their values.

        Returns:
            None.
        """
        raise NotImplementedError("Subclass must implement set_params method.")

    def get_name(self) -> str:
        """Returns the name of the model.

        Args:
            None.

        Returns:
            str: The name of the model.
        """
        return self.name

    def get_default_search_space(self):
        sp = SearchSpace()
        sp.from_default(self)
        return sp

    def get_model(self):
        """Returns the name of the model.

        Args:
            None.

        Returns:
            str: The name of the model.
        """
        return self.model

A base class for tunable machine learning models.

This class defines a set of abstract methods that must be implemented by any concrete subclass.

Attributes

None.

Ancestors

• abc.ABC

Subclasses

• BaseTunableForecaster
• TunableCatBoostRegressor
• TunableLGBMRegressor
• TunableRandomForestRegressor
• TunableSGDRegressor
• TunableXGBRegressor

Methods

def fit(self,
X: pandas.core.frame.DataFrame,
y: pandas.core.frame.DataFrame | pandas.core.series.Series)

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@abstractmethod
def fit(self, X: DataFrame, y: Union[DataFrame, Series]):
    """Fit the model to the training data.

    Args:
        X: The training data.
        y: The target values.

    Returns:
        None.
    """
    raise NotImplementedError("Subclass must implement fit method.")

Fit the model to the training data.

Args

X

The training data.

y

The target values.

Returns

None.

def get_default_search_space(self)

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def get_default_search_space(self):
    sp = SearchSpace()
    sp.from_default(self)
    return sp

def get_model(self)

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def get_model(self):
    """Returns the name of the model.

    Args:
        None.

    Returns:
        str: The name of the model.
    """
    return self.model

Returns the name of the model.

Args

None.

Returns

str

The name of the model.

def get_name(self) ‑> str

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def get_name(self) -> str:
    """Returns the name of the model.

    Args:
        None.

    Returns:
        str: The name of the model.
    """
    return self.name

Returns the name of the model.

Args

None.

Returns

str

The name of the model.

def get_params(self, deep: bool)

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@abstractmethod
def get_params(self, deep: bool):
    """Set the hyperparameters of the model.

    Args:
        params: hyperparameters and their values.

    Returns:
        None.
    """
    raise NotImplementedError("Subclass must implement set_params method.")

Set the hyperparameters of the model.

Args

params

hyperparameters and their values.

Returns

None.

def predict(self, X: pandas.core.frame.DataFrame)

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@abstractmethod
def predict(self, X: DataFrame):
    """Predict target values for the given data.

    Args:
        X: The input data.

    Returns:
        y_pred: The predicted target values.
    """
    raise NotImplementedError("Subclass must implement predict method.")

Predict target values for the given data.

Args

X

The input data.

Returns

y_pred

The predicted target values.

def set_params(self, params: Dict)

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@abstractmethod
def set_params(self, params: Dict):
    """Set the hyperparameters of the model.

    Args:
        params: hyperparameters and their values.

    Returns:
        None.
    """
    raise NotImplementedError("Subclass must implement set_params method.")

Set the hyperparameters of the model.

Args

params

hyperparameters and their values.

Returns

None.